Solving travelling salesman problem using black hole algorithm
- 390 Downloads
Over the last few decades, many nature-inspired algorithms have been proposed for solving complex and difficult problems. Each algorithm has its own merits and drawbacks. One of the most recent nature-inspired algorithms, which has been applied successfully in many applications, is black hole (BH) algorithm. BH algorithm is a population-based meta-heuristic algorithm that is inspired by the black hole phenomenon. It starts with a random population of solutions to the given optimization problem. The most excellent solution at each iteration which has the best fitness is chosen to be the black hole and the other form the stars. The black hole pulls the stars towards it and causes them to search the problem space for finding optimal solution. In this paper, the application of the BH algorithm on solving travelling salesman problem (TSP) is investigated. The aim of TSP is to find a tour in a set of cities in such a way, each city is visited exactly once and return to the starting city where the length of the tour is minimized. In order to evaluate the efficiency of the BH algorithm, it has been tested on several benchmark data sets and compared to other well-known algorithms. The experimental results show that the BH algorithm can find high-quality solutions compared to genetic algorithm, ant colony optimization and particle swarms optimization algorithms. Moreover, the BH algorithm is faster than other test algorithms.
KeywordsBlack hole algorithm Travelling salesman problem Meta-heuristic algorithms
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Adham MT, Bentley PJ (2014) An artificial ecosystem algorithm applied to the travelling salesman problem. In: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion. ACMGoogle Scholar
- Hatamlou A, Abdullah S, Nezamabadi-pour H (2011) Application of gravitational search algorithm on data clustering, rough sets and knowledge technology. Springer, BerlinGoogle Scholar
- Hatamlou A, Abdullah S, Hatamlou M (2011) Data clustering using big bang-big crunch algorithm. In: Communications in computer and information science, pp 383–388Google Scholar
- Hatamlou A, Abdullah S, Othman Z (2011) Gravitational search algorithm with heuristic search for clustering problems. In: 3rd conference on data mining and optimization (DMO)Google Scholar
- Heidari AA, Abbaspour RA (2014) Improved black hole algorithm for efficient low observable UCAV path planning in constrained aerospace. Adv Comput Sci Int J 3(3):87–92Google Scholar
- Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks: proceedingsGoogle Scholar
- Lenin K, Reddy BR, Kalavathi MS (2014) Black hole algorithm for solving optimal reactive power dispatch problem. Int J Res Manag Sci Technol 2:2321–3264Google Scholar
- Ouaarab A, Ahiod Bd, Yang XS (2014) Improved and discrete cuckoo search for solving the travelling salesman problem. In: Cuckoo search and firefly algorithm. Springer, Berlin, pp 63–84Google Scholar
- Sahana SK, Jain A (2014) High performance ant colony optimizer (HPACO) for travelling salesman problem (TSP). In: Tan Y, Shi Y, Coello CAC (eds) Advances in swarm intelligence. Springer, Berlin, pp 165–172Google Scholar
- Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, BristolGoogle Scholar
- Zong D, Wang K (2014) Hybrid nested partitions method for the traveling salesman problem. In: Wen Z, Li T (eds) Foundations of intelligent systems. Springer, Berlin, pp 55–67Google Scholar